DE102022106632A1 - Vehicle behavior planning for overtaking vehicles - Google Patents

Abstract

Systems, methods, and other example embodiments described herein relate to an improvement in vehicle behavior planning to avoid wrong-side overtaking maneuvers. In one embodiment, a method includes generating a driving context from sensor data about a surrounding environment of a host vehicle (100). The driving context identifies lanes of a road and a position of the host vehicle (100) in the lanes. The method includes, in response to a determination that the driving context and a state of a nearby vehicle meet a transition threshold, generating a trajectory (430) for the host vehicle (100) that includes passing on the wrong side of the vehicle in the avoids closeness. The method includes controlling one's own vehicle (100) according to the trajectory.

Description

technical field

The subject matter described herein relates generally to systems and methods for trajectory planning, and more particularly for identifying a driving context associated with wrong-side overtaking of large vehicles and generating trajectories to allow for transition and overtaking.

background

Wrong-side overtaking maneuvers, which occur when a first vehicle passes another vehicle in an inside lane (e.g., passing a vehicle traveling in a fast lane), can increase the risk of accidents. In general, the increased risk of the vehicle in the passing lane moving slower than traffic in the same lane, from increased blind spots along a corresponding side of the vehicle being passed, may be a reduced tendency for drivers to follow along a passing vehicle expected in an inside lane, etc. arise. In any event, this risk can be further exacerbated in circumstances where the vehicle being passed is larger, such as a truck or a vehicle larger than a standard passenger vehicle. For example, vans, delivery vans, garbage trucks, buses, trucks, etc. all represent vehicles of increased length that can be associated with increased risks of wrong-side overtaking. Accordingly, in the context of vehicles operating under autonomous or semi-autonomous controls, performing an unfamiliar wrong-side overtake maneuver can pose a significant hazard. That is, such a vehicle may continue to overtake on the wrong side without realizing the increased risk. Thus, difficulties remain in identifying and avoiding instances of wrong-side overtaking.

Summary

In one embodiment, example systems and methods associated with improving vehicle behavior planning to avoid wrong-side overtaking maneuvers are disclosed. As previously noted, overtaking on the wrong side can pose a significant safety hazard. That is, passing another vehicle in an inside lane may subject the passing vehicle to additional risks since the vehicle being passed may not recognize the presence of the passing vehicle due to such a maneuver being unusual and/or even prohibited in some areas of responsibility. Additionally, if the vehicle being passed is a truck (ie, semi-truck) or other large vehicle, this risk may be exacerbated due to increased blind spots or other difficulties.

Thus, in one embodiment, a disclosed approach includes determining when a host vehicle encounters a nearby vehicle traveling in a passing lane (i.e., in an adjacent outside lane) and generating a trajectory that includes a passing maneuver in the wrong lane Side by own vehicle avoids. For example, in at least one configuration, the host vehicle acquires and analyzes sensor data to determine a driving context. The driving context generally identifies a type of road on which the host vehicle is traveling (e.g., multi-lane versus single lane), a position of the host vehicle among lanes (e.g., a passing lane, a cruising lane, etc.), positions of nearby vehicles, and types of the Vehicles nearby (e.g. vehicle classes). In further arrangements, the driving context may also identify a condition of nearby vehicles, such as whether a nearby vehicle is currently attempting a lane change, which may be identified by active turn signals.

From this information, the host vehicle can identify circumstances where a wrong-side overtake may occur and generate a trajectory for autonomous or semi-autonomous control of the vehicle that avoids the wrong-side overtake. For example, if the subject vehicle identifies that the vehicle is traveling in a fast lane on a multi-lane road nearby, when the subject vehicle is in a cruising lane or lane (i.e., a slow lane or an inside lane), and proceeding in a wrong-side overtaking maneuver would result, the own vehicle then further considers whether to adjust a way to avoid the wrong-side overtaking maneuver or to proceed. In at least one configuration, the subject vehicle considers a class of the nearby vehicle and whether the nearby vehicle is attempting a lane change by identifying whether a turn signal is active. To the accordingly, if the nearby vehicle meets the transition threshold (e.g., if it is a Class 4 or larger vehicle with a turn signal active), the host vehicle may then generate a trajectory to provide sufficient space for the vehicle in nearby to transition while also changing lanes of own vehicle to overtake the nearby vehicle. In this way, one's own vehicle improves behavior planning in order to avoid overtaking maneuvers on the wrong side.

In one embodiment, a transition system is disclosed. The transition system includes one or more processors and memory communicatively coupled to the one or more processors. The memory stores a control module comprising instructions that, when executed by the one or more processors, cause the one or more processors to generate a driving context from sensor data about a surrounding environment of a host vehicle. The driving context identifies lanes of a road and a position of the subject vehicle in the lanes. The control module includes instructions to generate a host vehicle trajectory that avoids passing on the wrong side of the nearby vehicle, in response to a determination that the driving context and a state of a nearby vehicle meets a transition threshold. The control module includes instructions to control the host vehicle according to the trajectory.

In one embodiment, a non-transitory computer-readable medium is disclosed. The computer-readable medium stores instructions that, when executed by one or more processors, cause the one or more processors to perform the disclosed functions. The instructions include instructions to create a driving context from sensor data about a surrounding environment of a subject vehicle. The driving context identifies lanes of a road and a position of the subject vehicle in the lanes. The instructions include instructions to generate a host vehicle trajectory that avoids passing on the wrong side of the nearby vehicle, in response to a determination that the driving context and a state of a nearby vehicle meets a transition threshold. The instructions include instructions to control the own vehicle according to the trajectory.

In one embodiment, a method is disclosed. In one embodiment, a method includes generating a driving context from sensor data about a surrounding environment of a host vehicle. The driving context identifies lanes of a road and a position of the subject vehicle in the lanes. The method includes, in response to a determination that the driving context and a state of a nearby vehicle meets a transition threshold, generating a host vehicle trajectory that avoids overtaking on the wrong side of the nearby vehicle. The method includes controlling the host vehicle according to the trajectory.

character list

• 1 veranschaulicht ein Ausführungsbeispiel einer Konfiguration eines Fahrzeugs, in dem beispielhafte Systeme und Verfahren, die hier offenbart sind, arbeiten können.
• 2 veranschaulicht ein Ausführungsbeispiel eines Übergangssystems, das mit einem adaptiven Erzeugen von Bewegungsbahnen für ein Fahrzeug verbunden ist, um Überholmanöver auf der falschen Seite zu vermeiden.
• 3 veranschaulicht ein Ausführungsbeispiel eines Verfahrens, das mit einem Planen von Manövern eines Fahrzeugs verbunden ist.
• 4 veranschaulicht ein Beispiel eines Fahrkontexts, der einen Lastkraftwagen und ein eigenes Fahrzeug umfasst.

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate various systems, methods, and other embodiments of the disclosure. It will be appreciated that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one embodiment of the boundaries. In some embodiments, an element may be configured as multiple elements, or multiple elements may be configured as one element. In some embodiments, an element shown as an internal component of another element may be implemented as an external component, or vice versa. Furthermore, elements may not be drawn to scale.

• 1 illustrates one embodiment of a configuration of a vehicle in which example systems and methods disclosed herein may operate.
• 2 12 illustrates an embodiment of a transition system associated with adaptively generating trajectories for a vehicle to avoid wrong-side overtakes.
• 3 FIG. 11 illustrates an embodiment of a method associated with planning maneuvers of a vehicle.
• 4 Figure 12 illustrates an example of a driving context that includes a truck and a host vehicle.

Detailed description

Systems, methods, and other embodiments associated with improving vehicle behavior planning to avoid wrong-side overtaking maneuvers are disclosed. As noted above, overtaking maneuvers can be on the wrong side pose a significant security risk. That is, passing another vehicle in an inside lane may increase risks due to blind spots, an unexpected nature of the maneuver, and so on. In addition, when the vehicle being passed is a truck (ie, semi-trailer) or other large vehicle, the risk due to blind spots and other difficulties may be exacerbated.

Thus, in one embodiment, a disclosed approach includes determining when a host vehicle encounters a nearby vehicle traveling in a passing lane (i.e., in an adjacent outside lane) and generating a trajectory that includes a passing maneuver in the wrong lane Side by own vehicle avoids. For example, in at least one configuration, the host vehicle acquires and analyzes sensor data to determine a driving context. The driving context generally identifies a type of road on which one's vehicle is traveling (e.g., multi-lane or single-lane), a position of one's vehicle among lanes (e.g., a passing lane, a cruising lane, etc.), positions of nearby vehicles, and types of the Nearby vehicles (such as a vehicle class). The types generally define different sizes of vehicles according to a class. For example, a standard passenger vehicle may be a Class 2 vehicle, a city bus may be a Class 4 vehicle, and an articulated lorry (also referred to as a truck) may be a Class 8 or higher vehicle. In any case, the specific class of vehicle can also provide information about the risk associated with overtaking from the wrong side. Furthermore, the driving context may also identify a condition of nearby vehicles, such as whether a nearby vehicle is currently attempting a lane change, as can be identified from active turn signals, and a specific lane of separate nearby vehicles.

From this information, your own vehicle can identify circumstances in which an overtaking maneuver can happen on the wrong side. For example, if the subject vehicle identifies that the vehicle is driving nearby in a fast lane on a multi-lane road when the subject vehicle is in a cruising lane or lane (ie, in a slow lane or an inside lane), the subject vehicle can then further consider whether to adjust a way to avoid overtaking on the wrong side or to proceed. In at least one configuration, the host vehicle takes into account the class of the nearby vehicle and whether the nearby vehicle is attempting a lane change by identifying whether a turn signal is active and whether the class exceeds a defined transition threshold. If the nearby vehicle meets the transition threshold (for example, it is a Class 4 or larger vehicle with a turn signal active) and is actively indicating a lane change, the host vehicle may then generate a trajectory to provide sufficient space for providing the nearby vehicle to transition while also changing lanes of the own vehicle to overtake the nearby vehicle. According to this occurrence, the host vehicle generates, in at least one approach, the trajectory for autonomous control, semi-autonomous control, or simple guidance of a driver avoiding the wrong-side overtaking maneuver. In this way, one's own vehicle improves behavior planning in order to avoid overtaking maneuvers on the wrong side.

With reference to 1 an example of a vehicle 100 is illustrated. As used herein, a "vehicle" is any form of powered transportation. In one or more implementations, the vehicle 100 is an automobile. While arrangements are described herein with reference to motor vehicles, it will be appreciated that example embodiments are not limited to motor vehicles. In some implementations, the vehicle 100 may be any form of transportation that travels, for example, on a multi-lane highway with other vehicles, thus benefiting from the functionality discussed herein.

The vehicle 100 also includes various elements. It can be seen that in various exemplary embodiments, the vehicle 100 may not include all of the elements shown in 1 are shown may have. The vehicle 100 can have different combinations of the various elements included in 1 are shown. Furthermore, the vehicle 100 may have additional elements to those described in 1 are shown. In some arrangements, the vehicle 100 may be without one or more of the items listed in 1 are shown to be implemented. While the various elements as inside the vehicle are 100 in 1 are shown disposed, it will be appreciated that one or more of these elements may be disposed external to the vehicle 100 . Furthermore, the elements shown may be physically separated by large distances and considered distant controlled services (such as cloud computing services) are provided.

Some of the possible elements of the vehicle 100 are in 1 shown and described together with subsequent figures. A description of many of the items in 1 will after discussing the 2 until 4 provided for the sake of brevity of this description. In addition, it will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numbers have been repeated among the different figures to indicate corresponding, analogous, or similar elements. Furthermore, it is apparent that the embodiments described herein can be practiced using various combinations of the described elements.

In any event, the vehicle 100 includes a transition system 170 that operates to enhance behavior planning (i.e., trajectory generation) with respect to circumstances involving wrong-side overtaking maneuvers. Additionally, while shown as a standalone component, in one or more embodiments, the transition system 170 is integrated with the assistance system 160 or other similar system of the vehicle 100 to simplify functions of the other systems/modules. The noted functions and methods will become more apparent with further discussion of the figures.

Furthermore, the assistance system 160 may take many different forms, but generally provides some form of automated assistance to an operator of the vehicle 100 . For example, the assistance system 160 may include various advanced driver assistance system (ADAS) functions, such as lane keeping, adaptive cruise control, collision avoidance, emergency braking, etc. In further embodiments, the assistance system 160 may be a semi-autonomous or fully autonomous system that is partially or fully control the vehicle 100 . Accordingly, in whatever form, the assistance system 160 acts in cooperation with sensors of the sensor system 120 to obtain observations of the surrounding environment from which additional determinations can be derived to provide the various functions. Additionally, while the transition system 170 is generally discussed in relation to the assistance system 160, in at least one configuration, the transition system 170 may provide steering assistance without directly causing lateral or longitudinal steering of the vehicle. That is, the transition system 170 may instead provide warnings, alerts, visual guidance, or other non-invasive assistance to improve host vehicle 100 behaviors.

As a further aspect, the vehicle 100 also includes a communication system 180. In one embodiment, the communication system 180 communicates in accordance with one or more communication standards. For example, the communication system 180 may include multiple different antennas/transmitters/receivers and/or other hardware elements for communicating on different frequencies and according to respective protocols. In one arrangement, the communication system 180 communicates via short-range communications, such as a Bluetooth, WiFi, or other suitable protocol for communication between the vehicle 100 and other nearby devices (e.g., other vehicles). Also, in one arrangement, communication system 180 also communicates in accordance with a long-range protocol, such as Global System for Mobile Communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE), or other communication technology that allows vehicle 100 to communicate with a cloud-based resource. In any case, system 170 may take advantage of various wireless communication technologies to facilitate communications with nearby vehicles (e.g., vehicle-to-vehicle (V2V)), nearby infrastructure elements (e.g., vehicle-to-infrastructure (V2I)), and so on simplify. For example, in one or more arrangements, a nearby vehicle may communicate an intention to merge into a lane of host vehicle 100 without indicating an active turn signal. In this way, the subject vehicle 100 acquires information about a condition of the nearby vehicle without leveraging direct observations through the sensor data.

With reference to 2 An embodiment of transition system 170 is further illustrated. As shown, gateway system 170 includes a processor 110. Accordingly, processor 110 may be part of gateway system 170, or gateway system 170 may access processor 110 via a data bus or other communications path. In one or more embodiments, processor 110 is an application specific integrated circuit configured to provide functions to implement, which are connected to a control module 220. More generally, in one or more embodiments, processor 110 is an electronic processor, such as a microprocessor, capable of performing various functions as described herein when coded functions associated with gateway system 170 are executed will.

In one embodiment, transition system 170 includes memory 210 that stores control module 220 . Memory 210 is random access memory (RAM), read only memory (ROM), hard disk drive, flash memory, or other suitable memory for storing module 220. Module 220 consists of, for example, computer-readable instructions , which when executed by processor 110 cause processor 110 to perform the various functions disclosed herein. While in one or more embodiments, module 220 consists of instructions embodied in memory 210, in other embodiments module 220 includes hardware, such as processing components (e.g., controllers), circuitry, etc. for independently executing a or more of the above functions.

Furthermore, in one embodiment, the gateway system 170 includes a data store 230. The data store 230 is, in one arrangement, an electronically based data structure for storing information. For example, according to one approach, data store 230 is a database stored in memory 210 or other suitable medium and configured with routines executed by processor 110 for analysis of stored data, provision of stored data, a organization of stored data, etc. can be performed. In any event, in one embodiment, data store 230 stores data used by module 220 in performing various functions. For example, in one embodiment, data store 230 includes driving context 240 and sensor data 250 along with other information used by control module 220 .

Accordingly, the control module 220 generally includes instructions operative to control the processor 110 to obtain data inputs from one or more sensors of the vehicle 100 that form the sensor data 250 . In general, the sensor data 250 includes information representing observations of the surrounding environment of the vehicle 100 . Observations of the surrounding environment may include, in various embodiments, surrounding lanes, vehicles, objects, obstacles, etc. that may be present in the lanes, may be close to a road, may be within a parking lot, may be a garage structure, may be a driveway , or another area where the vehicle 100 is driving or parked.

While the control module 220 is described as controlling the various sensors to provide the sensor data 250, in one or more embodiments, the control module 220 may employ other techniques to obtain the sensor data 250 that are either active or passive. For example, the control module 220 may passively discover the sensor data 250 from a stream of electronic information provided by the various sensors to other components within the vehicle 100 . Externally, the control module 220 may take various approaches to merging data from multiple sensors when the sensor data 250 is provided. Thus, in one embodiment, the sensor data 250 represents a combination of insights gleaned from multiple sensors and/or other aspects of the vehicle 100 . For example, in another configuration, the sensor data 150 may include information obtained via the communication system 180, such as data from other vehicles and/or infrastructure devices about the average speed of traffic.

Whether the sensor data 250 is derived from a single sensor, multiple sensors, or is obtained through other means, the sensor data 250 includes various information that facilitates the control module 220, the driving context 240, a vehicle condition for a nearby vehicle etc. to determine. In addition, the sensor data 250 and the driving context 240 include an assessment of the surrounding environment around the own vehicle 100 , which in at least one approach includes 360 degrees around the own vehicle 100 . In this manner, the driving context 240 not only estimates the area in front of the host vehicle 100 but also the sides and rear to account for traffic and/or other hazards that may interfere with an adjustment of the vehicle 100 trajectory.

The control module 220 analyzes the sensor data 250 to determine the driving context 240 determine. In various approaches, the control module 220 implements machine learning algorithms (e.g. deep neural networks, such as convolutional neural networks (CNNs), concepts, heuristics and other processing mechanisms to analyze the sensor data 250 and the driving context 240 and other useful information thereof (e.g., vehicle states.) The driving context 240 generally defines a type of road that the host vehicle 100 is traveling on, along with other aspects of the surrounding environment, such as the presence of nearby vehicles, states of the nearby vehicles , classes of vehicles, etc. Regarding the type of road, the driving context 240 indicates the number of lanes, types of lanes (e.g., a passing lane, a cruise lane, an exit lane, etc.), highway or city setting, and so on.

It should be noted that as used in this disclosure, an inside lane is considered a cruise lane (also referred to as a slow lane), whereas a passing lane (also referred to as a fast lane) is instead generally considered to be a drive-by /overtaking by other vehicles. Additionally, the specific orientation of these lanes may vary depending on a specific country's driving policies (e.g., right-hand versus left-hand traffic). For example, in countries with right-hand traffic (e.g., the United States), passing lanes are on the left, while cruise lanes are on the right. In the case of a multi-lane road with two lanes for traffic in the same direction, a left or outside lane is the overtaking lane, whereas the right or inside lane is the cruising lane/slow lane. In circumstances where the number of lanes exceeds two in a given direction, this usage is generally applied to vehicle positions. That is, relative to the nearby vehicle being passed, a lane on the right is an inside lane, which may be referred to as a slow lane, while a lane on the left is an outside lane or passing lane. Of course, in cases of the nearby vehicle being in the left-most lane, the nearby vehicle is traveling in the outside lane/passing lane, with the inside/slow lane being the only option for a pass, resulting in an overtaking maneuver the wrong side. In the case of left-hand traffic, the given configurations are simply swapped.

In either case, the control module 220 acquires the sensor data 250, creating the driving context to assess the surrounding environment. As another embodiment for generating the driving context 240, the control module 220 further determines a vehicle state for any nearby vehicle. The vehicle state indicates the position, a classification quantity, a lane change status, and other aspects of the nearby vehicle (e.g., a trajectory, etc.). The position is generally the lane position and a distance from the subject vehicle 100. The classification size indicates how large the vehicle is nearby, with particular emphasis on length. As previously indicated, because longer length vehicles are associated with increased risks of wrong-side overtaking, the control module 220 classifies the nearby vehicle to quantify the length and thus risk of the wrong-side overtaking .

The vehicle's class is the rating assigned based on a Gross Vehicle Weight Rating (GVWR or ZGG), which generally corresponds to a vehicle's length. As such, the control module 220 may classify the nearby vehicle according to a standard, such as the Federal Highway Administration (FHWA) vehicle classification, which defines vehicles from Class 1-13. Classes include Class 1 - Motorcycles, Class 2 - Passenger Vehicles, Class 3 - Pickup Trucks/Vans, Class 4 - Buses, Class 5 - Two Axle Trucks, etc. Thus, vehicles that have a class rating above 4 are generally vehicles that have a larger have length. Of course, while the control module 220 may apply the class rating, in an alternative or additional arrangement the control module 220 may determine the length of the vehicle 100 separately. However, this determination may be difficult depending on traffic and a possibility of observing the vehicle nearby. Thus, for example, the control module 220 may apply the class rating according to estimates of the particular type of vehicle without a precise determination of length.

In either case, the control module 220 uses the driving context 240 to determine how to plan a trajectory for the host vehicle 100 . When host vehicle 100 is driving, control module 220 identifies a nearby vehicle and determines driving context 240 to estimate whether an overtaking maneuver is on the wrong track tian side will happen, the vehicle 100 should move on. That is, when the control module 220 identifies that the host vehicle 100 is traveling on a multi-lane road with a vehicle nearby in an outside lane, the control module 220 then estimates whether to proceed or adjust a trajectory to a Avoid overtaking on the wrong side.

Thus, in one or more configurations, the control module 220 determines whether the driving context 240 and the vehicle state of the nearby vehicle meet a transition threshold to determine how to proceed. For example, in one or more arrangements, the transition threshold defines base values for various aspects of the circumstance beyond which the control module 220 modifies the trajectory of the host vehicle 100 to avoid a wrong-side overtake. As such, the transition threshold generally defines aspects of the driving context 240, such as a multiple-lane road that is either urban or a highway, a relative position of the vehicle in proximity to the host vehicle 100 among lanes of the multiple-lane road (e.g., to an adjacent outboard lane), a length of the nearby vehicle (ie, class 4 or greater), and other characteristics indicating whether the nearby vehicle is transitioning or likely to transition back to the host vehicle 100 lane.

The further characteristics may include the nearby vehicle slowing down, activating a turn signal toward a lane of the host vehicle 100, and/or providing communication via V2V or other communication mechanism to the host vehicle 100 regarding an intention to change lanes. Accordingly, in response to a determination that the driving context 240 and the state of the nearby vehicle meet the transition threshold (i.e., the host vehicle 100 is in a condition associated with a wrong-side overtake and that nearby vehicle intends to change lanes), the control module 220 then generates a trajectory for the host vehicle that modifies a current path.

In one approach, the control module 220 generates the trajectory to provide a gap for the nearby vehicle to transition in front of the host vehicle 100 . Thus, the control module 220 may generate the trajectory to initially decelerate the host vehicle 100 to provide a gap into which the nearby vehicle will fit according to a length of the nearby vehicle. Accordingly, the control module 220 may use the class determination or an explicit determination of the length of the nearby vehicle to determine the gap size. In this way, the nearby vehicle is able to transition back into the lane, and the control module 220 can further generate the trajectory to perform a lane change either before or after the nearby vehicle transitions so that the host vehicle 100 can overtake instead of overtaking on the wrong side, improving safety.

In one approach, once the control module 220 generates the trajectory, the control module 220 implements the trajectory to control the host vehicle 100 by generating longitudinal and lateral controls that cause the host vehicle to follow the trajectory (e.g. via the assistance system 160 ). Of course, in cases where the host vehicle is not fully or semi-autonomous, the control module 220 may instead provide instructions to an operator via a head-up display or other human-machine interface (HMI) to implement the trajectory simplify.

Additional aspects of providing cooperative control according to a vehicle's system conditions are discussed with respect to FIG 3 discussed. 3 FIG. 3 illustrates a method 300 associated with planning maneuvers of a vehicle. The method 300 is described from the perspective of the transition system 170 according to FIG 1 discussed. While the method 300 will be discussed in connection with the gateway system 170, it will be appreciated that the method 300 is not limited to being implemented in the gateway system 170, but is instead an example of a system that the method 300 may implement.

At 310 , the control module 220 acquires the sensor data 250 . In one embodiment, acquiring the sensor data 250 includes controlling one or more sensors of the vehicle 100 to generate observations regarding the surrounding environment of the vehicle 100 . The control module 220, in one or more implementations, incrementally acquires the sensor data 250 from one or more sensors of the sensor system 120. The sensor data 250 includes observations of a surrounding environment of itself Vehicle 100, including specific regions relevant to identifying nearby vehicles and aspects related to avoiding wrong-side overtaking maneuvers. Additionally, the sensor data 250 further includes information about traffic and other hazards relevant to generating a trajectory of the vehicle 100 such that the vehicle 100 can determine the trajectory for a lane change maneuver without, for example, colliding with other vehicles or encountering other difficulties .

At 320, the control module 220 determines the driving context 240. In one arrangement, the control module 220 determines the driving context 240 by analyzing the sensor data 250 to determine a configuration of the lanes of the road and the position of the host vehicle 100 among the lanes. As another aspect, the control module 220 may also determine a vehicle condition from one or more nearby vehicles. The vehicle status identifies the nearby vehicle(s) and whether the nearby vehicle(s) is/are at least a Class 4 vehicle(s), as noted above. In other aspects, the vehicle state also indicates trajectories, blink states, and so on.

At 330, the control module 220 determines whether the driving context 240 meets the transition threshold. In one approach, the control module 220 initially determines whether the driving context 240 meets general transition threshold conditions, such as whether the road is a multi-lane road (e.g., at least two lanes of traffic in the same direction), and whether the nearby vehicle is on an adjacent one outer lane is positioned. If the above conditions are met, the control module 220 may then proceed to 340 by determining the condition of the nearby vehicle. Otherwise, the control module 220 proceeds to obtain the sensor data 250 in 310 and repeat the above functions.

At 340, the control module 220 may further determine the vehicle condition of the nearby vehicle. The control module 220 may determine the vehicle state as a part of the driving context 240 or separately upon a determination that the driving context meets the transition threshold. In each case, the vehicle state defines aspects specific to the nearby vehicle that is the subject of the wrong-side overtake. For example, in one configuration, the control module 220 determines indicators as to whether the nearby vehicle is attempting to merge into a lane of the host vehicle 100 . The indicators may include nearby vehicle slowing down, nearby vehicle activating a turn signal, lateral trajectory associated with a lane change, etc. Furthermore, as part of the vehicle state, the control module 220 may also include a length and/or identify a class of nearby vehicle.

At 350, the control module 220 determines whether the vehicle state meets the transition threshold. In one arrangement, the control module 220 determines whether a class of the nearby vehicle is at least a size class 4. In other words, the control module 220 determines whether a length of the nearby vehicle exceeds a transition threshold (eg, greater than 25 feet). As another aspect of determining whether the vehicle state meets the transition threshold, the control module 220 may determine whether the nearby vehicle intends to change lanes. In one arrangement, the control module 220 determines intent to change lanes according to the aforementioned characteristics such as a turn signal, trajectory, V2V communication, etc. Thus, when the vehicle condition meets the aforementioned class/length and intent characteristics, lanes to transition, the control module 220 indicates that the vehicle state meets the transition threshold and proceeds to generate the trajectory at 360 . Otherwise, the control module restarts the method 300 .

At 360, the control module 220 generates a trajectory for the host vehicle 100 that avoids overtaking on the wrong side of the nearby vehicle. In at least one arrangement, generating the trajectory includes determining a length of the nearby vehicle and generating the trajectory with a gap in front of host vehicle 100 that accounts for the length and into which the nearby vehicle can transition. The control module 220 may already know the length from previous determinations, or the control module 220 may actively determine the length/size as mentioned above. In another aspect, the control module 220 generates the trajectory with a lane change maneuver to an adjacent lane in which the nearby vehicle is/has been traveling while maintaining a gap in a current lane for the nearby vehicle. Generating the trajectory with the lane change maneuver can also identify of traffic that may affect the lane change (e.g., traffic approaching from behind) to adjust speeds/timing of the lane change.

In 370, the control module 220 controls the host vehicle according to the trajectory. In one arrangement, the control module 220 provides a control input to the host vehicle 100 to control a path of the host vehicle 100 according to the trajectory. The assistance system 160 may initiate the controls according to a request from the control module 220 according to the movement trajectory. The controls may vary depending on the specific mode of operation of host vehicle 100 (eg, autonomous, semi-autonomous, etc.), but may include lateral controls to cause vehicle 100 to follow the trajectory, may provide automatic braking/acceleration etc. In this way, the transition system 170 avoids safety hazards by avoiding overtaking maneuvers on the wrong side.

As a further explanation of how the systems and methods disclosed herein work 4 considered. 4 Figure 4 illustrates a multi-lane road 400, which may be a freeway or a multi-lane surface urban road. As shown, the host vehicle 100 is traveling in a lane to the inside of a truck 410 that is in a passing lane/fast lane. Accordingly, host vehicle 100 is currently on a trajectory 420 that is a wrong-side overtaking maneuver. However, the transition system 170 recognizes that the driving context 240 includes a multi-lane road with a nearby vehicle in an outboard adjacent lane, consistent with the noted approach. Thus, as host vehicle 100 approaches, transition system 170 may further determine that truck 410 meets the class indicated by the transition threshold and also determine that the truck intends to transition into host vehicle's 100 lane. As such, the transition system 170 creates a trajectory 430 that provides a gap for the truck 410 to transition and also for the host vehicle 100 to perform a lane change so that the host vehicle 100 can overtake the truck 410 once the truck 410 passed. In this way, host vehicle 100 improves safety while also potentially improving traffic flow by allowing truck 410 to change lanes.

In addition, it can be seen that the transition system 170 from 1 configured in various arrangements with separate integrated circuits and/or electronic chips. In such embodiments, the control module 220 is embodied as a separate integrated circuit. The circuits are connected via connecting paths to ensure communication signals between the separate circuits. Of course, while separate integrated circuits are discussed, in various embodiments, circuits may be integrated into a common integrated circuit and/or integrated circuit board. Additionally, the integrated circuits can be combined into fewer integrated circuits or split into more integrated circuits. In other embodiments, portions of the functionality associated with module 220 may be embodied as firmware executable by a processor and stored in non-transitory memory. In further embodiments, the module 220 is integrated as hardware components of the processor 110 .

In another embodiment, the methods described and/or their equivalents may be implemented using computer-executable instructions. Thus, in one embodiment, a non-transitory computer-readable medium is configured with stored computer-executable instructions that, when executed by a machine (e.g., processor, computer, etc.), cause the machine (and/or associated components) to perform the method to execute.

While for ease of explanation the illustrated methodologies are shown in the figures and described as a sequence of blocks, it will be appreciated that the methodologies are not limited by the order of the blocks, as some blocks may be in different orders and/or concurrently with other blocks from those shown and described. Also, less than all of the blocks illustrated may be used to implement an example methodology. Blocks can be combined or separated into multiple components. Furthermore, additional and/or alternative methodologies may employ additional blocks that are not illustrated.

1 is discussed in detail below as an example environment in which the system and methods disclosed herein may operate. In some cases, the vehicle 100 is configured to be selective between an autonomous one To switch mode, one or more semi-autonomous modes and / or a manual mode. Such switching can be implemented in any suitable manner. “Manual mode” means that all or most of the navigation and/or maneuvering of the vehicle is performed in accordance with inputs received from a user (e.g., a human driver).

In one or more embodiments, the vehicle 100 is an autonomous vehicle. As used herein, “autonomous vehicle” refers to a vehicle operating in an autonomous mode. “Autonomous mode” refers to navigating and/or maneuvering the vehicle 100 along a driving route using one or more computing systems to control the vehicle 100 with minimal or no input from a human driver. In one or more embodiments, the vehicle 100 is fully automated. In one embodiment, the vehicle 100 is configured with one or more semi-autonomous modes, wherein one or more computer systems perform a portion of navigating and/or maneuvering the vehicle 100 along a travel route, with a vehicle operator (i.e., a driver) providing input to the vehicle to perform a portion of navigating and/or maneuvering vehicle 100 along a travel route. Such semi-autonomous operation may include supervisory control implemented by the transition system 170 to ensure that the vehicle 100 remains within defined state constraints.

The vehicle 100 may include a processor 110 and multiple processors 110 . In one or more arrangements, the processor(s) 110 may be a main processor of the vehicle 100 . For example, processor(s) 110 may be an electronic control unit (ECU). The vehicle 100 may include one or more data stores 115 (e.g., data store 230) for storing one or more types of data. Data storage 115 may include volatile and/or non-volatile memory. Examples of suitable data storage 115 include RAM (random access memory), flash memory, ROM (read only memory), PROM (programmable read only memory), EPROM (erasable programmable read only memory), EEPROM (Electrically Erasable Programmable Read Only Memory), registers, magnetic disks, optical disks, hard disks, or any other suitable storage medium, or any combination thereof. Data storage 115 may be a component of processor(s) 110, or data storage 115 may be operably connected to processor(s) 110 for use thereby. The term "operably connected" as used throughout this specification may include direct or indirect connections, including connections without direct physical contact.

In one or more arrangements, the one or more data stores 115 may include map data. The map data may include maps of one or more geographic areas. In some cases, the map data may include information (e.g., metadata, labels, etc.) about roads, traffic control devices, road markings, structures, features, and/or landmarks in the one or more geographic areas. In some cases, the map data may include aerial/satellite views. In some cases, the map data may include ground views of an area, including 360 degree ground views. The map data may include measurements, dimensions, distances, and/or information for one or more items included in the map data and/or related to other items included in the map data. The map data can include a digital map with information about a road geometry. The map data may also include feature-based map data, such as information about relative positions of buildings, curbs, posts, etc. In one or more arrangements, the map data may include one or more terrain maps. In one or more arrangements, the map data may include one or more static obstacle images. The static obstacle map(s) may include information about one or more static obstacles located within one or more geographic areas. A "static obstacle" is a physical object whose position does not change or does not change significantly over a period of time and/or whose size does not change or does not change significantly over a period of time. Examples of static obstacles include trees, buildings, curbs, fences, railings, medians, utility poles, statues, monuments, traffic signs, benches, furniture, mailboxes, large rocks, hills. The static obstacles can be objects that extend beyond ground level.

The one or more data stores 115 may include sensor data (e.g., sensor data 250). In this context, "sensor data" means any information from the sensors equipped with the vehicle 100, including the capabilities and other information about such sensors.

As mentioned above, the vehicle 100 may include the sensor system 120 . Sensor system 120 may include one sensor or multiple sensors. “Sensor” means any device, component, and/or system that can sense, sense, and/or feel anything. The one or more sensors may be configured to operate in real time. As used herein, the term "real-time" means a level of processing responsiveness that a user or system perceives as sufficiently immediate for a particular operation or determination to be performed, or that allows the processor to keep up with an external operation.

In arrangements where the sensor system 120 includes a plurality of sensors, the sensors can operate independently of one another. Alternatively, two or more of the sensors can work in combination. In such a case, the two or more sensors can form a sensor network. The sensor system 120 and/or the one or more sensors may be operable with the processor(s) 110, the data storage(s) 115, and/or another element of the vehicle 100 (including any of the elements described in 1 are shown) connected. The sensor system 120 may acquire data from at least a portion of the vehicle's 100 external environment.

Sensor system 120 may include any suitable type of sensor. Various examples of different types of sensors are described here. However, it is understood that the exemplary embodiments are not limited to the specific sensors that are described. Sensor system 120 may include one or more vehicle sensors 121 . The vehicle sensor(s) 121 can sense, determine, and/or sense information about the vehicle 100 itself or about interiors of the vehicle 100 . In one or more arrangements, vehicle sensor(s) 121 may be configured to sense and/or sense changes in position and orientation of vehicle 100, such as based on inertial acceleration. In one or more arrangements, vehicle sensor(s) 121 may include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system and/or other suitable sensors. The vehicle sensor(s) 121 may be configured to detect and/or sense one or more characteristics of the vehicle 100 . In one or more arrangements, the vehicle sensor(s) 121 may include a speed measuring device for determining a current speed of the vehicle 100 . In addition, the vehicle sensor system 121 may include sensors within a passenger compartment, such as in-seat pressure/weight sensors, seat belt sensors, camera(s), etc.

Alternatively or additionally, the sensor system 121 may include one or more environmental sensors 122 configured to acquire and/or sense driving environment data. "Driving environment data" includes data or information about an external environment in which an autonomous vehicle or one or more portions thereof is/are located. For example, the one or more environmental sensors 122 may be configured to sense and/or sense obstacles in at least a portion of the external environment of the vehicle 100 and/or information/data about such obstacles. Such obstacles can be stationary objects and/or dynamic objects. The one or more environmental sensors 122 may be configured to sense and/or sense other things in the external environment of the vehicle 100, such as lane markings, signs, traffic lights, road signs, lane lines, crosswalks, curbs near the vehicle 100, terrain objects, etc.

Various examples of sensors of sensor system 120 are described herein. The example sensors may be part of the one or more environment sensors 122 and/or the one or more vehicle sensors 121 . However, it is understood that the exemplary embodiments are not limited to the specific sensors that are described. As an example, in one or more arrangements, the sensor system 121 can include one or more radars sensors, one or more LIDAR sensors, one or more sonar sensors, and/or one or more cameras. In one or more arrangements, the one or more cameras may be high dynamic range (HDR) cameras or infrared (IR) cameras.

The vehicle 100 may include an input system 130 . An “input system” includes, but is not limited to, any device, component, system, element, or assembly, or group thereof, that allows information/data to be input into a machine. The input system 130 may receive input from a vehicle passenger (e.g., an operator or passenger). The vehicle 100 may include an output system 140 . An "output system" includes any device, component, or arrangement or groups thereof that enable information/data to be presented to a vehicle occupant (e.g., a person, a vehicle occupant, etc.).

The vehicle 100 may include one or more vehicle systems 150 . Various examples of the one or more vehicle systems 150 are shown in FIG 1 shown, however, the vehicle 100 may include a different combination of systems that is illustrated in the example provided. In one example, the vehicle 100 may include a propulsion system, a braking system, a steering system, a throttle system, a transmission system, a signaling system, a navigation system, and so on. The specified systems may, separately or in combination, include one or more devices, one or more structural elements, and/or a combination thereof.

For example, the navigation system may include one or more devices, one or more applications, and/or combinations thereof configured to determine the geographic location of the vehicle 100 and/or to determine a driving route for the vehicle 100 . The navigation system may include one or more mapping applications to determine a driving route for the vehicle 100 . The navigation system may include a global positioning system, a local positioning system, or a geolocation system.

The processor(s) 110, the gateway system 170, and/or the assistance system 160 may be operably connected to communicate with the various vehicle systems 150 and/or individual components thereof. For example, if back to 1 is gone, the processor(s) 110 and/or the assistance system 160 may be in communication to send and/or receive information from the various vehicle systems 150 to indicate movement, speed, maneuvering, heading, direction etc. of the vehicle 100 to control. The processor(s) 110, gateway system 170, and/or assistance system 160 may control some or all of these vehicle systems 150, and thus may be partially or fully autonomous.

The processor(s) 110, the gateway system 170, and/or the assistance system 160 may be operably connected to communicate with the various vehicle systems 150 and/or individual components thereof. For example, if to 1 is reversed, the processor(s) 110, the transition system 170, and/or the assistance system 160 may be in communication to send and/or receive information from the various vehicle systems 150 to determine movement, speed, maneuvering, to control the heading, direction, etc. of the vehicle 100 . The processor(s) 110, the gateway system 170, and/or the assistance system 160 may control some or all of these vehicle systems 150.

The processor(s) 110, transition system 170, and/or assistance system 160 may be operable to control navigation and/or maneuvering of the vehicle 100 by controlling one or more of the vehicle systems 150 and/or associated components will become. For example, when operating in an autonomous mode, the processor(s) 110, the transition system 170, and/or the assistance system 160 may control the direction and/or speed of the vehicle 100. The processor(s) 110, the transition system 170, and/or the assistance system 160 may cause the vehicle 100 to accelerate (e.g., by increasing the input of energy provided to the engine), decelerate (e.g., by a reducing the supply of energy to the engine and/or by applying brakes) and/or changing direction (e.g. by turning the front two wheels).

Additionally, the transition system 170 and/or the assistance system 160 may operate to perform various driving-related tasks. The vehicle 100 may include one or more actuators. the activ or any combination of elements operable to adjust and/or modify one or more of the vehicle's systems or associated components in response to received signals or other inputs from the processor(s) Processors 110 and/or the assistance system 160 to react. Any suitable actuator can be used. For example, the one or more actuators may include motors, pneumatic actuators, hydraulic pistons, relays, solenoids, and/or piezoelectric actuators, to name just a few possibilities.

The vehicle 100 may include one or more modules, at least some of which are described herein. The modules may be implemented as computer-readable program code that, when executed by a processor 110, implements one or more of the various operations described herein. One or more of the modules may be a component of processor(s) 110, or one or more of the modules may execute on other processing systems and/or be distributed among other processing systems with which processor(s) 110 is operably connected is/are. The modules may include instructions (e.g., program logic) executable by one or more processors 110 . Alternatively or in addition, one or more data stores 115 may include such instructions.

In one or more arrangements, one or more of the modules described herein may include artificial or computer-based intelligence elements, such as a neural network, fuzzy logic, or other machine learning algorithms. Furthermore, in one or more arrangements, one or more of the modules may be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.

Vehicle 100 may include one or more modules that form assistance system 160 . The assistance system 160 may be configured to receive data from the sensor system 120 and/or any other type of system capable of receiving information related to the vehicle 100 and/or the external environment of the vehicle 100 . In one or more arrangements, the assistance system 160 can use such data to generate one or more driving scenery models. Assistance system 160 can determine the position and speed of vehicle 100 . The assistance system 160 can determine the location of obstacles or other environmental features, including traffic signs, trees, bushes, neighboring vehicles, pedestrians, etc.

The assistance system 160 may be configured to receive and/or determine location information for obstacles within the external environment of the vehicle 100 for use by the processor(s) 110 and/or one or more of the modules described herein. may be configured to estimate a position and orientation of the vehicle 100, a vehicle position in global coordinates based on signals from a plurality of satellites, or any other data and/or signals that can be used to determine the current state of the vehicle 100 or to determine the position of the vehicle 100 in relation to the associated environment, for use in either generating a map or in determining the position of the vehicle 100 in relation to map data.

The assistance system 160 may be configured either independently or in combination with the transition system 170 to generate a route(s), current autonomous driving maneuvers for the vehicle 100, future autonomous driving maneuvers, and/or modifications to current autonomous driving maneuvers based on data provided by the Sensor system 120 may be obtained from driving scene models and/or from data from any other suitable source, such as determinations from sensor data 250 to determine. "Driving Maneuver" means an action or actions that affects the movement of a Vehicle. Examples of driving maneuvers include: accelerating, decelerating, braking, turning, moving in a lateral direction of the vehicle 100, changing lanes, transitioning into a lane, and/or reversing, just to name a few possibilities . The assistance system 160 can be configured to implement specific driving maneuvers. The assistance system 160 can cause such autonomous driving maneuvers to be implemented directly or indirectly. As used herein, "causing" or "causing" means causing, commanding, directing, and/or enabling an event or action to occur, or at least being in a state for such event or action to occur such an action may occur, either in a direct or in an indirect manner. The assistance system 160 can be configured perform various vehicle functions and/or transmit data to, receive data from, interact with, and/or control vehicle 100 or one or more associated systems (e.g., one or more of vehicle systems 150). .

Detailed example embodiments are disclosed herein. However, it should be understood that the disclosed embodiments are intended to be exemplary only. Accordingly, specific structural and functional details disclosed herein are not to be interpreted as limiting, but are to be interpreted as a basis for the claims and as a representative basis for teaching one skilled in the art to use the aspects depicted herein speaking any suitable detailed structure in various ways. Furthermore, the terms and expressions used herein are not intended to be limiting, but rather to provide an understandable description of possible implementations. Various embodiments are in the 1-4 shown, but the exemplary embodiments are not limited to the illustrated structure or application.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations, systems, methods, and computer program products according to the various embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown sequentially may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order depending on the functionality involved.

The systems, devices and/or processes described above may be implemented in hardware or in a combination of hardware and software and may be implemented in a centralized manner in one processing system or in a distributed manner in which different elements distributed across multiple interconnected processing systems. Any type of processing system or other device adapted to perform the methods described herein is suitable. A combination of hardware and software may be a processing system having computer usable program code that, when loaded and executed, controls the processing system to perform the methods described herein. The systems, devices, and/or processes may also be embodied in computer-readable storage, such as a computer program product or other data program storage device readable by a machine, a program of instructions executable by the machine to describe methods and processes , described herein, is materially embodied. These elements can also be embedded in an application product that includes all the features that allow the implementation of the methods described herein and that, when loaded in a processing system, is capable of executing these methods.

Furthermore, arrangements described herein may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied, for example stored, thereon. Any combination of one or more computer-readable media can be used. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The term "computer-readable storage medium" means a non-transitory storage medium. A computer-readable medium may take forms including, but not limited to, non-transitory media and transient media. Nonperishable media may include, for example, optical disks, magnetic disks, and so on. Ephemeral media may include, for example, semiconductor memory, dynamic memory, and so on. Examples of such computer-readable medium may include, but are not limited to, a floppy disk, flexible disk, hard disk, magnetic tape, other magnetic medium, ASIC, CD, other optical medium, RAM, ROM, a memory chip or memory card, memory stick, and other media from which a computer, processor, or other electronic device can read. In the context of this document, a computer-readable storage medium can be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The following description includes definitions of select terms used herein. The definitions encompass various examples and/or forms of building elements that fall within the scope of an expression and that can be used for various implementations. The examples are not intended to be limiting. Both singular and plural forms of terms may be within the definitions.

References to "a single embodiment", "one embodiment", "a single example", "an example", etc. indicate that the embodiment(s) or example(s) so described have a particular feature, a particular structure, characteristic, property, element, or limitation, but not each embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation includes certain limitation. Furthermore, repeated use of the phrase "in one embodiment" is not necessarily referring to the same embodiment, although it may be.

"Module" as used herein includes a computer or electrical hardware device(s), firmware, non-transitory computer-readable medium storing instructions, and/or combinations of these devices configured to provide function(s). or to perform an action(s), and/or cause a function or action of other logic, method and/or system. A module may include a microprocessor controlled by an algorithm, discrete logic (e.g., an ASIC), analog circuitry, digital circuitry, a programmed logic device, a memory device containing instructions that, when executed, generate an algorithm execute, etc. include. A module, in one or more embodiments, includes one or more CMOS gates, a combination of gates, or other circuit components. When multiple modules are described, one or more example embodiments include incorporating the multiple modules into a physical modular device. Similarly, when a single module is described, one or more embodiments distribute the single module among multiple physical devices.

Additionally, as used herein, a module includes routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific data types. In further configurations, a memory generally stores said modules. The memory associated with a module may be a buffer or cache memory embedded within a processor, RAM, ROM, flash memory, or other suitable electronic storage medium. In further aspects, a module as contemplated by the present disclosure is implemented as an application specific integrated circuit (ASIC), a hardware component of a system on a chip (SoC), a programmable logic array (PLA), or other suitable hardware component embedded with a defined set of configurations (e.g., instructions) for performing the disclosed functions.

In one or more arrangements, one or more of the modules described herein may include artificial or computer-based intelligence elements, such as a neural network, fuzzy logic, or other machine learning algorithms. Furthermore, in one or more arrangements, one or more of the modules may be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.

Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire-based, optical fiber, cable, RF, etc., or any suitable combination of the above. Computer program code for performing operations for embodiments of the present arrangements may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java™, Smalltalk, C++, or the like, or conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code may reside entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or run on a server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be to an external computer (for example, through the Internet using an Internet service provider ) getting produced.

The terms "a" and "an" as used herein are defined as one or more than one. The term "plurality" as used herein is defined as two or more than two. The term "another" as used herein is defined as at least one second or more. As used herein, the terms “comprise” and/or “have” are defined as including (ie, open language). The term "at least one of ... and ..." as used herein refers to and includes any and all possible combinations of one or more of the listed items associated therewith. As an example, the phrase "at least one of A, B, and C" includes only A, only B, only C, or any combination thereof (e.g., AB, AC, BC, or ABC).

Embodiments disclosed herein may be embodied in other forms without departing from the scope or essential attributes thereof. Accordingly, reference is made to the following claims, rather than to the foregoing specification, for an indication of the scope thereof.

Systems, methods, and other example embodiments described herein relate to an improvement in vehicle behavior planning to avoid wrong-side overtaking maneuvers. In one embodiment, a method includes generating a driving context from sensor data about a surrounding environment of a host vehicle (100). The driving context identifies lanes of a road and a position of the host vehicle (100) in the lanes. The method includes, in response to a determination that the driving context and a state of a nearby vehicle meet a transition threshold, generating a trajectory (430) for the host vehicle (100) that includes passing on the wrong side of the vehicle in the avoids closeness. The method includes controlling one's own vehicle (100) according to the trajectory.

Claims (20)

Transition system with: one or more processors (110); and a memory (210) communicatively coupled to the one or more processors and storing: a control module (220) comprising instructions that when executed by the one or more processors cause the one or more processors to: generate a driving context from sensor data about a surrounding environment of a host vehicle (100), the driving context identifying lanes of a road and a position of the host vehicle in the lanes; in response to a determination that the driving context and a state of a nearby vehicle meet a transition threshold, generate a host vehicle trajectory (430) that avoids passing on the wrong side of the nearby vehicle; and to control the own vehicle according to the trajectory. transition system after claim 1 , wherein the control module comprises instructions to generate the trajectory comprising instructions to determine a length of the vehicle in the vicinity and generate the trajectory with a gap in front of the host vehicle that takes into account the length and into which the vehicle is in can pass nearby. transition system after claim 1 or 2 wherein the control module comprises instructions to generate the trajectory comprising instructions to generate the trajectory with a lane change maneuver to an adjacent lane of the nearby vehicle while maintaining a gap in a current lane for the nearby vehicle . Transitional system according to one of the Claims 1 until 3 wherein the control module includes instructions to generate the driving context including instructions to analyze the sensor data to determine a configuration of the lanes of the road and the position of the host vehicle among the lanes. Transitional system according to one of the Claims 1 until 4 , wherein the control module comprises instructions to determine that the driving context satisfies the transition threshold comprising instructions to identify that the host vehicle position is in a lane of the road with at least one passing lane at an outboard host vehicle position. Transitional system according to one of the Claims 1 until 5 , wherein the control module includes instructions to determine that the condition of the nearby vehicle meets the transition threshold comprising instructions to identify that a class of the nearby vehicle is at least a size class 4, and that the vehicle in is driving nearby in an adjacent outside lane. Transitional system according to one of the Claims 1 until 6 wherein the control module includes instructions to create the driving context including instructions to identify the nearby vehicle and identify that the nearby vehicle is at least a Class 4 vehicle. Transitional system according to one of the Claims 1 until 7 , wherein the control module includes instructions for using at least one sensor (120) to obtain the sensor data about the surrounding environment of the own vehicle, and wherein the control module includes instructions for controlling the own vehicle according to the trajectory by longitudinal and lateral Controls are generated that cause the own vehicle to follow the trajectory. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors (110), cause the one or more processors to: generate a driving context from sensor data about a surrounding environment of a host vehicle (100), the driving context identifying lanes of a road and a position of the host vehicle in the lanes; in response to a determination that the driving context and a state of a nearby vehicle meet a transition threshold, generate a host vehicle trajectory (430) that avoids passing on the wrong side of the nearby vehicle; and to control the own vehicle according to the trajectory. Non-transitory computer-readable medium claim 9 wherein the instructions to generate the trajectory include instructions to determine a length of the nearby vehicle and to generate the trajectory with a gap in front of the own vehicle that takes the length into account and into which the nearby vehicle can transition . Non-transitory computer-readable medium claim 9 or 10 wherein the instructions to generate the trajectory include instructions to generate the trajectory with a lane change maneuver to an adjacent lane of the nearby vehicle while maintaining a gap in a current lane for the nearby vehicle. Non-transitory computer-readable medium according to any one of claims 9 until 11 wherein the instructions to generate the driving context include instructions to analyze the sensor data to determine a configuration of the lanes of the road and the position of the host vehicle among the lanes. procedure with: generating a driving context from sensor data about a surrounding environment of a subject vehicle (100), the driving context identifying lanes of a road and a position of the subject vehicle in the lanes; in response to a determination that the driving context and a state of a nearby vehicle meet a transition threshold, generating a host vehicle trajectory (430) that avoids overtaking on the wrong side of the nearby vehicle; and controlling the own vehicle according to the trajectory. procedure after Claim 13 wherein generating the trajectory includes determining a length of the nearby vehicle and generating the trajectory with a gap in front of one's own vehicle that takes the length into account and into which the nearby vehicle can transition. procedure after Claim 13 or 14 wherein generating the trajectory includes generating the trajectory with a lane change maneuver to an adjacent lane of the nearby vehicle while maintaining a gap in a current lane for the nearby vehicle. Procedure according to one of Claims 13 until 15 , wherein generating the driving context comprises analyzing the sensor data to determine a configuration of the lanes of the road and the position of the host vehicle among the lanes. Procedure according to one of Claims 13 until 16 , wherein determining that the driving context satisfies the transition threshold comprises identifying that the host vehicle's position in a lane of the road with at least an overtaking lane at an outboard position of the own vehicle. Procedure according to one of Claims 13 until 17 , wherein determining that the condition of the nearby vehicle meets the transition threshold comprises identifying that a class of the nearby vehicle is at least magnitude 4 and that the nearby vehicle is traveling in an adjacent outside lane. Procedure according to one of Claims 13 until 18 , wherein generating the driving context includes identifying the nearby vehicle and identifying that the nearby vehicle is at least a Class 4 vehicle. Procedure according to one of Claims 13 until 19 , further comprising: obtaining using at least one sensor (120) the sensor data about the surrounding environment of the own vehicle, wherein controlling the own vehicle according to the trajectory comprises generating longitudinal and lateral controls that cause the own vehicle follows the trajectory.

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